Method and System for Modeling Behavior of a Visitor to An E-Commerce Location

• US 20110119108A1
• Filed: 11/08/2010
• Published: 05/19/2011
• Est. Priority Date: 04/30/1999
• Status: Abandoned Application

• Current Assignee: SmartSignal Corporation (Emerson Electric Company)
• Original Assignee: SmartSignal Corporation (Emerson Electric Company)
• Inventors: Black, Christopher L., Hines, J. Wesley
• Application Number:
  US12/941,658
• Publication Number:
  US 20110119108A1
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A method for modeling the behavior of a visitor to an e-commerce location includes the steps of automatically obtaining one or more visitor characteristic values, and automatically developing a model of the visitor'"'"'s behavior according to a nonlinear state estimation technique (NSET). The method also includes then automatically estimating a set of visitor behavior characteristic values with said model that model said visitor'"'"'s behavior.

Claims

1. A method for modeling the behavior of a visitor to an e-commerce location, comprising the steps of:

automatically obtaining one or more visitor characteristic values;

automatically developing a model of the visitor'"'"'s behavior according to a nonlinear state estimation technique (NSET); and

automatically estimating a set of visitor behavior characteristic values with said model that model said visitor'"'"'s behavior.

2. The method of claim 1, further comprising the step of predicting said visitor'"'"'s future behavior at said e-commerce location based on said set of behavior characteristic values.

3. The method of claim 1, further comprising the steps of:

determining a set of residuals between said set of estimated behavior characteristic values and a set of actual behavior values; and

statistically monitoring said set of residuals.

4. The method of claim 3, further comprising the step of adjusting said NSET to compensate for said residuals.

5. The method of claim 3, further comprising the step of adjusting said e-commerce location to compensate for residuals between the desired behavior and the predicted behavior.

6. The method of claim 5, wherein adjusting said e-commerce location comprises adjusting goods, services and/or advertising provided at said e-commerce locations.

7. The method of claim 1, further comprising providing goods, services, and/or advertising at said e-commerce location based on said estimated behavior characteristic values.

8. The method of claim 1, wherein developing a model further comprises the step of selecting a representative sample data set based on said visitor characteristic values from a set of statistical characteristic data within a historical database.

9. The method of claim 8, further comprising the step of populating a prototype matrix with vectors comprising said statistical characteristic data and inverting said prototype matrix according to said NSET.

10. The method of claim 1, wherein said visitor characteristic values comprise visitor demographic information and visitor purchase habits.

11. The method of claim 1, wherein said nonlinear state estimation technique functions as a regressive model.

12. The method of claim 11, wherein said regressive model is not an iterative process and comprises at least one operation selected from the group consisting of single matrix multiplication, inversion and decomposition.

13. The method of claim 1, further comprising the step of:

selectively determining a set of variables for said model; and

compactly representing said model as a matrix of observed states of said set of variables for said model.

14. The method of claim 13, wherein selectively determining a set of variables for said model comprises discarding variables that demonstrate an effect on said model below a threshold value.

15. The method of claim 1, further comprising the steps of:

measuring a set of actual behavior values for said visitor based on said visitor'"'"'s actual behavior at said e-commerce location; and

comparing said set of estimated behavior characteristic values and said set of actual behavior values with at least one similarity operators.

16. The method of claim 15, wherein said similarity operators are selected from the group consisting of:

Bernoulli difference;

Relative entropy;

Euclidean norm;

City block distance;

Linear correlation coefficient;

Common mean linear correlation coefficient;

Root mean power error;

Scaled mean power error; and

Matrix multiplication.

17. The method of claim 16, further comprising the steps of:

selectively determining a set of variables for said model; and

compactly representing said model as a matrix of observed states of said set of variables for said model.

18. The method of claim 17, wherein selectively determining a set of variables for said model comprises discarding variables that demonstrate an effect on said model below a threshold value.

19. A system for modeling the behavior of a visitor to an e-commerce location, comprising:

operational instructions stored on a memory and executable by a processing module for obtaining one or more visitor characteristic values;

operational instructions stored on a memory and executable by a processing module for a NSET for developing a model of the visitor'"'"'s behavior; and

operational instructions stored on a memory and executable by a processing module for estimating a set of visitor behavior characteristic values with said model that model said visitor'"'"'s behavior.

20. The system of claim 19, further comprising operational instructions for predicting said visitor'"'"'s future behavior at said e-commerce location based on said set of behavior characteristic values.

21. The system of claim 19, further comprising:

operational instructions for determining a set of residuals between said set of estimated behavior characteristic values and a set of actual behavior values; and

operational instructions for statistically monitoring said set of residuals.

22. The system of claim 21, further comprising operational instructions for adjusting said NSET to compensate for said residuals.

23. The system of claim 19, further comprising:

operational instructions for determining a set of residuals between said set of estimated behavior characteristic values and a set of desired behavior values; and

operational instructions for statistically monitoring said set of residuals.

24. The system of claim 23, further comprising the step of adjusting said e-commerce location to compensate for said residuals.

25. The system of claim 24, wherein adjusting said e-commerce location comprises adjusting goods, services and/or advertising provided at said e-commerce locations.

26. The system of claim 19, further comprising operational instructions for providing goods, services, and/or advertising at said e-commerce location based on said estimated behavior characteristic values.

27. The system of claim 19, further comprising a processing module to execute operational instructions and memory operationally coupled to said processing module for storing data and operational instructions.

28. The system of claim 19, wherein developing a model further comprises selecting a representative sample data set based on said visitor characteristic values from a set of statistical characteristic data within a historical database.

29. The system of claim 28, further, comprising operational instructions for populating a prototype matrix with vectors comprising said statistical characteristic data and inverting said prototype matrix according to said NSET.

30. The system of claim 19, wherein said visitor characteristic values comprise visitor demographic information and visitor purchase habits.

31. The system of claim 19, wherein said nonlinear state estimation technique functions as a regressive model.

32. The system of claim 31, wherein said regressive modeling is not an iterative process and comprises at least one operation selected from the group consisting of single matrix multiplication, inversion and decomposition.

33. The system of claim 19, further comprising:

operational instructions for selectively determining a set of variables for said model; and

operational instructions for compactly representing said model as a matrix of observed states of said set of variables for said model.

34. The system of claim 33, wherein selectively determining a set of variables for said model comprises discarding variables that demonstrate an effect on said model below a threshold value.

35. The system of claim 19, further comprising:

operational instructions for measuring a set of actual behavior values for said visitor based on said visitor'"'"'s actual behavior at said e-commerce location; and

operational instructions for comparing said set of estimated behavior characteristic values and said set of actual behavior values with at least one similarity operators.

36. The system of claim 35, wherein said similarity operators are selected from the group consisting of:

Bernoulli difference;

Relative entropy;

Euclidean norm;

City block distance;

Linear correlation coefficient;

Common mean linear correlation coefficient;

Root mean power error;

Scaled mean power error; and

Matrix multiplication.

37. The system of claim 36, further comprising:

operational instructions for selectively determining a set of variables for said model; and

operational instructions for compactly representing said model as a matrix of observed states of said set of variables for said model.

38. The system of claim 37, wherein selectively determining a set of variables for said model comprises discarding variables that demonstrate an effect on said model below a threshold value.